LearnTF/mnist_restructured/mnist_train.py

# -*- coding: utf8 -*-
import os

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference

# define input, output, batch and training params

BATCH_SIZE = 1000
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99

MODEL_SAVE_PATH = "model/"
MODEL_NAME = "model.ckpt"

# train a fully connected neural network
def train(mnist):
    x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')

    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
    y = mnist_inference.inference(x, regularizer)
    global_step = tf.Variable(0, trainable=False)

    # moving average, cross entropy, loss function with regularization and learning rate
    variable_average = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    variable_average_op = variable_average.apply(tf.trainable_variables())
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
    cross_entropy_mean = tf.reduce_mean(cross_entropy)
    loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE,
        LEARNING_RATE_DECAY
    )

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
    with tf.control_dependencies([train_step, variable_average_op]):
        train_op = tf.no_op(name='train')

    # initialize persistence class
    saver = tf.train.Saver()

    config = tf.ConfigProto(allow_soft_placement=True)
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.4)
    config.gpu_options.allow_growth = True
    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())
        # create directory
        try:
            os.mkdir(MODEL_SAVE_PATH)
        except:
            print("directory already exist")

        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)

            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})

            if i % 2500 == 0:
                print("after %d training step(s), loss on training batch is %g " % (step, loss_value))
                saver.save(
                    sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step
                )


def main(argv=None):
    print("start")
    mnist = input_data.read_data_sets("../MNIST_data", one_hot=True)
    print("start")
    train(mnist)


if __name__ == '__main__':
    tf.app.run()